Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the combination and mapping patterns of these design features for the purposes of design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that limit the theorical development and industrial application of DICD, such as specific dataset building, engineering design related feature engineering, systematic methods and techniques for DICD implementation, more entry points for DICD application in the entire product design life cycle, etc. In this regard, a systematic theorical reference for DICD implementation is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The works provide a brief research status, key research topics, and more importantly a general road map for DICD implementation.

数据驱动的智能计算设计（DICD）是利用深度学习算法从历史或虚构的设计过程数据中提取和表示设计特征，以实现设计解决方案的检索、生成、优化、评估等目的的研究热点。本文建立了DICD实现的系统理论参考，包括DICD 项目计划的一般工作流程，DICD 项目实施的整体框架，DICD 实施的计算机制，详细DICD 实施的关键启用技术，以及DICD的三个应用场景。

数据驱动的智能计算设计：方法、技术及应用